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The Oracles of Delphi and Nostradamus are early examples of often ambiguous forecasters. In the 17 th century Sir William Petty discerned a seven-year business cycle, suggesting a basis for systematic economic forecasts. In the USA a forecasting industry developed around but much of it was wiped out by the Great Depression — which it failed to foresee!

Macroeconomic forecasting as we know it today was a product of the Keynesian revolution. Official forecasts were produced regularly soon after World War II in the Scandinavian countries, and the practice spread to the UK in the early s and most other advanced economies by the s. Macroeconomic forecasting requires macroeconomists and macroeconomic data.

Economic forecasting |

The Australian Treasury started to recruit specialist economists by the late s. A brief discussion of prospects for the forthcoming year first appeared in the supporting statements to the budget in They did not evolve into an explicit table of detailed forecasts until In the early s Treasury and the Reserve Bank started regular meetings to discuss macroeconomic forecasts. At the same time as judgemental forecasting was being developed, more formal approaches were being made possible by the construction of econometric models of the Australian economy.

A small national accounting model had been estimated by Trevor Swan , in the Department of Post-War Reconstruction. NIF was described at the time as a medium-sized model, with 97 behavioural equations. A new stream of modelling was commenced in Treasury around The Treasury Macroeconomic Model TRYM is smaller 25 estimated behavioural equations , with more emphasis on a theoretical basis for equations and their steady-state properties.

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Most equations are specified in an error correction model format which makes a clear distinction between short- and long-run properties. Some subsets of equations were estimated as a system; Taplin et al From the budget was switched from August to May, making the task of forecasting a fiscal year that runs from July to June somewhat harder. Treasury generally has two major forecasting rounds in the year. Two other rounds are also held, around June and December. As they come soon after the intensive forecasting for the budget and MYEFO usually published in November respectively, these rounds tend to be less formal.

Sectoral analysts continually monitor economic developments, assessing these data for their implications for the forecasts. Before the start of each forecasting round, information is gathered from business liaison visits. A typical liaison round involves around meetings over a two-week period. The forecasting round gathers pace with the release of the quarterly national accounts, about two months after the end of the quarter to which they refer.

The national accounts form the framework for the forecasting exercise. The technical assumptions about interest rates, exchange rates and oil prices are reset in the light of recent developments. Any changes to fiscal policy are also incorporated.

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A range of other statistical releases from the Australian Bureau of Statistics and information from other government departments and private sector surveys are also analysed. Sectoral teams within the Domestic Economy Division assess the implications of all this information for their forecasts using a mix of single-equation econometric models, partial indicators for example, retail sales for private consumption , leading indicators for example, housing loan approvals for dwelling investment , business surveys forexample, private sector surveys covering confidence and hiring intentions and the ABS survey of capital expenditure intentions , and advice from specialist agencies such as the Australian Bureau of Agricultural and Resource Economics and the Tax Office.

Handbook of Economic Forecasting, Volume 1

The judgement is informed by economic theory and assessments of recent economic analysis. The teams discuss their forecasts with their colleagues and management and present them to a meeting of the division for review. Another perspective is supplied at these meetings from the forecasts generated by the TRYM model.

An iterative process is used to ensure consistency between the various elements of the forecast for example, employment is an important influence on private consumption and hence GDP, but GDP also strongly influences employment.

Financial Forecast - Economic Forecast - Time Series - Structural Model

The individual sector forecasts are linked together using the National Accounts Forecasting Framework, rather than in an econometric model, although the TRYM is also used as a consistency check. An internal quality test of the forecasts follows by presenting them to senior management within Treasury. They are then subject to an external quality test at the Joint Economic Forecasting Group meeting, where the forecasts are discussed with representatives from the Reserve Bank, the Australian Bureau of Statistics and the Departments of the Prime Minister and Cabinet and Finance and Administration.

Perhaps the main lesson that has been drawn from the history of forecasting is the need for appropriate humility about the task. To adapt an adage attributed to Socrates, economic forecasters may be wiser than are other commentators as they are at least aware of the extent of the uncertainties. The consensus from the economic literature evaluating economic forecasts is that they are generally somewhat better than extrapolating trends. They tend to be better for macroeconomic variables such as inflation and GDP than for financial variables such as exchange rates or stockmarket prices.

But they tend to have significant difficulties with large turning points. But while accepting that forecasting is difficult, forecasters should keep trying to improve.

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  • Analysing forecast errors can be an important part of this process. Finding that an econometric model, or a set of judgements, has been consistently overpredicting may be a sign that the approach has missed some structural change in the economy. Much discussion of forecasts focuses on the point estimates, but arguably more important is the accompanying narrative about the forecasts.

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    Tossing a coin involves risk but not uncertainty, as while the outcome of a future toss is not known, the probability of tossing heads is known to be a half. This distinction can be applied to the forecasting process. For example, the outlook for the Australian economy may be influenced by whether the Federal Reserve raises US interest rates. Data from US financial markets might indicate there is, say, a one-in-four chance of US interest rates rising by 25 basis points, which could be regarded as posing a risk to the forecasts of the Australian economy.

    But in addition there is uncertainty about how the exchange rate would respond to a basis-point rise in US interest rates, how much global demand would fall in response to the interest rate rise, and so forth.

    When these risks and uncertainties are considered, rather than a single point forecast, a probability distribution of forecasts around the central most likely forecast can be constructed. Calculating these distributions is a complicated matter. One relatively simple way of presenting the probability distribution is just to look at the past record of forecasting errors and assume that current forecasts will have a similar degree of accuracy.

    So, for example, if the average error on previous GDP forecasts had been 0. Recent advances in economic theory and computing power mean that it is now feasible to go beyond this and present probability distributions that allow for the fact that there may be more risks and uncertainties at some times than others. For example, the Asian financial crisis was unexpected and unprecedented and so it would be reasonable to think there was more uncertainty around forecasts in October than there had been in May This more sophisticated approach also allows for the possibility that there may be more downside risks around the most likely outcome than upside risks, that is, that the probability distribution may be not be symmetric.

    The budget papers do not explicitly spell out a probability distribution, but have gradually given more space to discussion of risks and uncertainties around the forecasts. Economic forecasting is difficult. But producing forecasts is necessary, as they are an essential underlying feature of the budget. Have macroeconomic forecasts grown more or less accurate over time? This paper assembles, examines, and interprets evidence bearing on this question.

    Contrary to some critics, there are no indications that U. Neither do any definite trends in a positive direction emerge from comparisons of annual and quarterly multiperiod forecasts and time-series projections for the principal aggregative variables. The argument is developed and to some extent documented that major failures of forecasting are related to the incidence of slowdowns and contractions in general economic activity.

    Not only the forecasts of real GNP growth and unemployment but also those of nominal GNP growth and inflation often go seriously wrong when such setbacks occur.

    Economic Forecasting and Policy

    Forecasters tend to rely heavily on the persistence of trends in spending, output, and the price level. More attention to data and techniques that are sensitive to business cycle movements and turning points could help improve their record. Published: Zarnowitz, Victor.